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---
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_margin_1mm_spanpred
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rule_learning_margin_1mm_spanpred
This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the enoriega/odinsynth_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3250
- Margin Accuracy: 0.8518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2000
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Margin Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| 0.5448 | 0.16 | 20 | 0.5229 | 0.7717 |
| 0.4571 | 0.32 | 40 | 0.4292 | 0.8109 |
| 0.4296 | 0.48 | 60 | 0.4009 | 0.8193 |
| 0.4028 | 0.64 | 80 | 0.3855 | 0.8296 |
| 0.3878 | 0.8 | 100 | 0.3757 | 0.8334 |
| 0.3831 | 0.96 | 120 | 0.3643 | 0.8367 |
| 0.3591 | 1.12 | 140 | 0.3582 | 0.8393 |
| 0.3598 | 1.28 | 160 | 0.3533 | 0.8401 |
| 0.3635 | 1.44 | 180 | 0.3442 | 0.8427 |
| 0.3478 | 1.6 | 200 | 0.3406 | 0.8472 |
| 0.342 | 1.76 | 220 | 0.3352 | 0.8479 |
| 0.3327 | 1.92 | 240 | 0.3352 | 0.8486 |
| 0.3487 | 2.08 | 260 | 0.3293 | 0.8487 |
| 0.3387 | 2.24 | 280 | 0.3298 | 0.8496 |
| 0.3457 | 2.4 | 300 | 0.3279 | 0.8505 |
| 0.3483 | 2.56 | 320 | 0.3286 | 0.8510 |
| 0.3421 | 2.72 | 340 | 0.3245 | 0.8517 |
| 0.3332 | 2.88 | 360 | 0.3252 | 0.8517 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
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